To lower the computational barriers for NAS research, [1]} composed NAS-Bench 101, a dataset consisting of 423k unique neural network architectures and their performance evaluated on the CIFAR-10 dataset. This allows for benchmarking NAS algorithms without the necessity to train candidate models from scratch. Likewise, NAS-Bench-201 [2]} operates on a cell-based search space, NAS-Bench-301 [3]} on DARTS, and NAS-Bench-360 [4]}, on a more diverse set of tasks than CIFAR and Imagenet classification. The strategy proposed in this study is evaluated on NAS-Bench-201 in order to allow the most fair comparison with the strategies evaluated in [5]} using NASlib.
